An Integrated Model of External Validity Usability Evaluations in Health Care
Bibliographic record
Abstract
External validity is the extent to which the findings of an experimental study are applicable or can be generalized to other people and contexts. External validity includes both population validity (i.e., the representativeness of the sample) and ecological validity (i.e., the representativeness of other contextual variables such as the stimuli, workflow, and environment). There is no consensus on the number and names of the ecological validity dimensions researchers should consider when designing or evaluating usability evaluations in health care. Therefore, in this paper, we integrated concepts from 3 ecological validity frameworks into a unified external validity. We used this new framework to describe the dimensions of external validity of a previous usability study. This framework can inform the design and description of usability evaluations to enhance reproducibility and comparison of findings and ultimately better understand the impact of different dimensions and external validity holistically.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.144 | 0.235 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".